• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

基于模糊可能性聚类的无监督数据分析：在医学图像 MRI 中的应用。

Unsupervised approach data analysis based on fuzzy possibilistic clustering: application to medical image MRI.

机构信息

LRIT-CNRST URAC 29, Mohammed V-Agdal University, Faculty of Science, BP 1014, Rabat, Morocco.

LRIT-CNRST URAC 29, Mohammed V-Agdal University, Faculty of Science, BP 1014, Rabat, Morocco ; LARIT Equipe Imagerie et Multimedia, Ibn Tofail University, Faculty of Science, ENCG, BP 242, Kénitra, Morocco.

出版信息

Comput Intell Neurosci. 2013;2013:435497. doi: 10.1155/2013/435497. Epub 2013 Dec 29.

DOI:10.1155/2013/435497
PMID:24489535
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3891616/
Abstract

The analysis and processing of large data are a challenge for researchers. Several approaches have been used to model these complex data, and they are based on some mathematical theories: fuzzy, probabilistic, possibilistic, and evidence theories. In this work, we propose a new unsupervised classification approach that combines the fuzzy and possibilistic theories; our purpose is to overcome the problems of uncertain data in complex systems. We used the membership function of fuzzy c-means (FCM) to initialize the parameters of possibilistic c-means (PCM), in order to solve the problem of coinciding clusters that are generated by PCM and also overcome the weakness of FCM to noise. To validate our approach, we used several validity indexes and we compared them with other conventional classification algorithms: fuzzy c-means, possibilistic c-means, and possibilistic fuzzy c-means. The experiments were realized on different synthetics data sets and real brain MR images.

摘要

大数据的分析和处理对研究人员来说是一个挑战。已经有几种方法被用来对这些复杂的数据进行建模，它们基于一些数学理论：模糊理论、概率理论、可能性理论和证据理论。在这项工作中，我们提出了一种新的无监督分类方法，它结合了模糊理论和可能性理论；我们的目的是克服复杂系统中不确定数据的问题。我们使用模糊 C 均值（FCM）的隶属函数来初始化可能性 C 均值（PCM）的参数，以解决由 PCM 生成的聚类重合的问题，并克服 FCM 对噪声的弱点。为了验证我们的方法，我们使用了几种有效性指标，并将其与其他传统分类算法进行了比较：模糊 C 均值、可能性 C 均值和可能性模糊 C 均值。实验在不同的合成数据集和真实的脑磁共振图像上进行。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fdc/3891616/d36675b98528/CIN2013-435497.alg.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fdc/3891616/df3e8aef88c2/CIN2013-435497.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fdc/3891616/39867af1ca07/CIN2013-435497.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fdc/3891616/459e451e273a/CIN2013-435497.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fdc/3891616/1164bb815573/CIN2013-435497.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fdc/3891616/fca73db7e879/CIN2013-435497.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fdc/3891616/9f283fa639d8/CIN2013-435497.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fdc/3891616/4358d03892b8/CIN2013-435497.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fdc/3891616/e8ad958c504f/CIN2013-435497.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fdc/3891616/d42619191f56/CIN2013-435497.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fdc/3891616/d22d998a9130/CIN2013-435497.alg.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fdc/3891616/67d066cbfe3a/CIN2013-435497.alg.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fdc/3891616/b49d5bd51cfe/CIN2013-435497.alg.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fdc/3891616/d36675b98528/CIN2013-435497.alg.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fdc/3891616/df3e8aef88c2/CIN2013-435497.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fdc/3891616/39867af1ca07/CIN2013-435497.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fdc/3891616/459e451e273a/CIN2013-435497.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fdc/3891616/1164bb815573/CIN2013-435497.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fdc/3891616/fca73db7e879/CIN2013-435497.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fdc/3891616/9f283fa639d8/CIN2013-435497.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fdc/3891616/4358d03892b8/CIN2013-435497.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fdc/3891616/e8ad958c504f/CIN2013-435497.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fdc/3891616/d42619191f56/CIN2013-435497.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fdc/3891616/d22d998a9130/CIN2013-435497.alg.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fdc/3891616/67d066cbfe3a/CIN2013-435497.alg.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fdc/3891616/b49d5bd51cfe/CIN2013-435497.alg.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fdc/3891616/d36675b98528/CIN2013-435497.alg.004.jpg

相似文献

1
Unsupervised approach data analysis based on fuzzy possibilistic clustering: application to medical image MRI.基于模糊可能性聚类的无监督数据分析：在医学图像 MRI 中的应用。
Comput Intell Neurosci. 2013;2013:435497. doi: 10.1155/2013/435497. Epub 2013 Dec 29.
2
Rough set based generalized fuzzy c-means algorithm and quantitative indices.基于粗糙集的广义模糊 c 均值算法及量化指标。
IEEE Trans Syst Man Cybern B Cybern. 2007 Dec;37(6):1529-40. doi: 10.1109/tsmcb.2007.906578.
3
An Efficient Segmentation and Classification System in Medical Images Using Intuitionist Possibilistic Fuzzy C-Mean Clustering and Fuzzy SVM Algorithm.基于直觉模糊可能性聚类和模糊 SVM 算法的医学图像高效分割与分类系统。
Sensors (Basel). 2020 Jul 13;20(14):3903. doi: 10.3390/s20143903.
4
Similarity Measure-Based Possibilistic FCM With Label Information for Brain MRI Segmentation.基于相似度测度的带标签信息的脑 MRI 分割可能性模糊 C 均值。
IEEE Trans Cybern. 2019 Jul;49(7):2618-2630. doi: 10.1109/TCYB.2018.2830977. Epub 2018 May 21.
5
A modified possibilistic fuzzy c-means clustering algorithm for bias field estimation and segmentation of brain MR image.一种用于脑磁共振图像偏置场估计和分割的改进可能性模糊 C 均值聚类算法。
Comput Med Imaging Graph. 2011 Jul;35(5):383-97. doi: 10.1016/j.compmedimag.2010.12.001. Epub 2011 Jan 22.
6
A segmentation of brain MRI images utilizing intensity and contextual information by Markov random field.利用马尔可夫随机场的强度和上下文信息对脑 MRI 图像进行分割。
Comput Assist Surg (Abingdon). 2017 Dec;22(sup1):200-211. doi: 10.1080/24699322.2017.1389398. Epub 2017 Oct 26.
7
Automated detection of white matter changes in elderly people using fuzzy, geostatistical, and information combining models.使用模糊、地质统计学和信息融合模型自动检测老年人白质变化。
IEEE Trans Inf Technol Biomed. 2011 Mar;15(2):242-50. doi: 10.1109/TITB.2010.2081996. Epub 2010 Sep 30.
8
[MR brain image segmentation based on modified fuzzy C-means clustering using fuzzy GIbbs random field].基于使用模糊吉布斯随机场的改进模糊C均值聚类的磁共振脑图像分割
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2008 Dec;25(6):1264-70.
9
A Hybrid Method for Image Segmentation Based on Artificial Fish Swarm Algorithm and Fuzzy c-Means Clustering.一种基于人工鱼群算法和模糊c均值聚类的图像分割混合方法。
Comput Math Methods Med. 2015;2015:120495. doi: 10.1155/2015/120495. Epub 2015 Nov 16.
10
Suspicious Lesion Segmentation on Brain, Mammograms and Breast MR Images Using New Optimized Spatial Feature Based Super-Pixel Fuzzy C-Means Clustering.基于新型优化空间特征的超像素模糊 C 均值聚类的脑、乳腺 X 线摄影术及乳腺磁共振图像可疑病变分割。
J Digit Imaging. 2019 Apr;32(2):322-335. doi: 10.1007/s10278-018-0149-9.

本文引用的文献

1
Multimodal classification of Alzheimer's disease and mild cognitive impairment.阿尔茨海默病和轻度认知障碍的多模态分类。
Neuroimage. 2011 Apr 1;55(3):856-67. doi: 10.1016/j.neuroimage.2011.01.008. Epub 2011 Jan 12.
2
Multiscale image segmentation by integrated edge and region detection.基于边缘和区域检测的多尺度图像分割。
IEEE Trans Image Process. 1997;6(5):642-55. doi: 10.1109/83.568922.
3
Automatic image segmentation by integrating color-edge extraction and seeded region growing.通过整合颜色边缘提取和种子区域生长实现自动图像分割。
IEEE Trans Image Process. 2001;10(10):1454-66. doi: 10.1109/83.951532.
4
Active contours without edges.无边缘活动轮廓。
IEEE Trans Image Process. 2001;10(2):266-77. doi: 10.1109/83.902291.
5
Symmetric region growing.对称区域增长。
IEEE Trans Image Process. 2003;12(9):1007-15. doi: 10.1109/TIP.2003.815258.
6
Model-based 3-D segmentation of multiple sclerosis lesions in magnetic resonance brain images.基于模型的磁共振脑图像多发性硬化病变的三维分割。
IEEE Trans Med Imaging. 1995;14(3):442-53. doi: 10.1109/42.414608.
7
Survey of clustering algorithms.聚类算法综述
IEEE Trans Neural Netw. 2005 May;16(3):645-78. doi: 10.1109/TNN.2005.845141.